Privacy preserving existence recognition and construction of hypertree agent organization

Abstract

Decentralized probabilistic reasoning, constraint reasoning, and decision theoretic reasoning are some essential tasks of cooperative multiagent systems. Several frameworks for these tasks organize agents into a junction tree (JT). We show that existing techniques for JT existence recognition and construction leak information on private variables, shared variables, agent identities and adjacency, that can potentially be protected. We present a scheme to quantify these privacy losses. We develop two novel algorithms for JT existence recognition and for JT construction when existing, that provide strong guarantee of agent privacy. Our experimental comparison shows that the proposed algorithms out-perform existing techniques, one of them having the lowest privacy loss and the other having no privacy loss, while being more efficient than most alternatives.